检索结果-内蒙古大学图书馆

Generative and contrastive graph representation learning with message passing

NEURAL NETWORKS 2025年 185卷 107224页

作者： Tang, Ying Yang, Yining Sun, Guodao Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310000 Zhejiang Peoples R China

Self-supervised graph representation learning (SSGRL) has emerged as a promising approach for graph embeddings because it does not rely on manual labels. SSGRL methods are generally divided into generative and contrastive approaches. Generative methods often suffer from poor graph quality, while contrastive methods, which compare augmented views, are more resistant to noise. However, the performance of contrastive methods depends heavily on well-designed data augmentation and high-quality negative samples. Pure generative or contrastive methods alone cannot balance both robustness and performance. To address these issues, we propose a self-supervised graph representation learning method that integrates generative and contrastive ideas, namely Contrastive Generative Message Passing graph Learning (CGMP-GL). CGMP-GL incorporates the concept of contrast into the generative model and message aggregation module, enhancing the discriminability of node representations by aligning positive samples and separating negative samples. On one hand, CGMP-GL integrates multi-granularity topology and feature information through cross-view multi-level contrast while reconstructing masked node features. On the other hand, CGMP-GL optimizes node representations through self-supervised contrastive message passing, thereby enhancing model performance in various downstream tasks. Extensive experiments over multiple datasets and downstream tasks demonstrate the effectiveness and robustness of CGMP-GL.

关键词： Self-supervised graph representation learning graph autoencoder Contrastive learning Message passing

来源：评论

学校读者我要写书评

暂无评论

A novel dynamic spatio-temporal graph based condition monitoring framework for consistency retention of digital twin

引用

JOURNAL OF MANUFACTURING SYSTEMS 2025年 79卷 455-465页

作者： Wang, Xiaofeng Yan, Jihong Xu, Xun Harbin Inst Technol Sch Mechatron Engn Harbin 150001 Peoples R China Univ Auckland Dept Mech Engn Auckland New Zealand

A responsive consistency retention strategy is crucial for the engineering application of digital twin (DT). The condition monitoring technique based on graph theory can provide an overall reliability assessment and thus guide DT model updating. However, most existing studies constructed graph topology merely based on data information without incorporating prior engineering knowledge, which restricts the performance of such approaches. To tackle this limitation, a novel graph construction paradigm based on the mechanism of performance degradation and fault propagation is developed in this study. On this basis, unsupervised learning is further combined to forma dynamic spatio-temporal graph based condition monitoring framework for DT consistency retention. Specifically, the spatial dependencies of multi-sensors are quantified based on the evolution of the fault-related frequency band, and then multidomain features are assigned to each graph node. After that, the spatio-temporal graph set is fed to a dual-decoder graph autoencoder to extract the essential features of normal conditions, where a domain adaptation module is introduced to eliminate environmental effects. Hypothesis testing is conducted at last to inspect the machine state over time and make the final decision. Validation and comprehensive comparison experiments were carried out on two engineering scenarios with different scales (component and system level). The Numenta Anomaly Benchmark (NAB) was employed to evaluate the effectiveness of the proposed approach and the results revealed the great potential of the proposed framework for DT consistency retention.

关键词： Digital twin Spatio-temporal graph graph autoencoder Condition monitoring

来源：评论

学校读者我要写书评

暂无评论

WDCIP:spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic

引用

地球空间信息科学学报（英文版） 2024年第6期27卷中插16,2023-2047页

作者： Siqi Wang Xiaoxiao Zhao Jingyu Qiu Haofen Wang Chuang Tao College of Design and Innovation Tongji UniversityShanghaiChina Product Development Department Wayz AI Technology Company LimitedShanghaiChina

The outbreak and subsequent recurring waves of COVID-19 pose threats on the emergency management and people's daily life,while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic ***,some challenges remain to be addressed in terms of multi-source heterogeneous data fusion,deep mining,and comprehensive *** Spatio-Temporal Artificial Intelligence(STAI)technology,which focuses on integrating spatial related time-series data,artificial intelligence models,and digital tools to provide intelligent computing platforms and applications,opens up new opportunities for scientific epidemic *** this end,we leverage STAI and long-term experience in location-based intelligent services in the ***,we devise and develop a STAI-driven digital infrastructure,namely,WAYZ Disease Control Intelligent Platform(WDCIP),which consists of a systematic framework for building pipelines from auto-matic spatio-temporal data collection,processing to AI-based analysis and inference imple-mentation for providing appropriate applications serving various epidemic *** to the platform implementation logic,our work can be performed and summarized from three aspects:(1)a STAI-driven integrated system;(2)a hybrid GNN-based approach for hierarchical risk assessment(as the core algorithm of WDCIP);and(3)comprehensive applica-tions for social epidemic *** work makes a pivotal contribution to facilitating the aggregation and full utilization of spatio-temporal epidemic data from multiple sources,where the real-time human mobility data generated by high-precision mobile positioning plays a vital role in sensing the spread of the *** far,WDCIP has accumulated more than 200 million users who have been served in life convenience and decision-making during the pandemic.

关键词： COVID-19 spatio-temporal artificial intelligence epidemic prevention and control platform risk assessment SIR graph autoencoder graph representation learning human mobility

来源：评论

学校读者我要写书评

暂无评论

MoRGH: movie recommender system using GNNs on heterogeneous graphs

引用

KNOWLEDGE AND INFORMATION SYSTEMS 2024年第12期66卷 7419-7435页

作者： Ziaee, Seyed Sina Rahmani, Hossein Nazari, Mohammad Iran Univ Sci & Technol Sch Comp Engn Tehran *** Iran Univ Calgary Dept Comp Sci Calgary T2N 1N4 AB Canada

Nowadays, with the advent of movies and TV shows and the competition between different movie streamer companies and movie databases to attract more users, movie recommenders have become a major prerequisite for customer satisfaction. Most of the previously introduced methods used collaborative, content-based, and hybrid filtering techniques, where neural network-based approaches and matrix completion are the major approaches of most recent movie recommender systems. The major drawbacks of previous systems are not considering side information, such as plot synopsis and cold start problem, in the context of movie recommendations. In this paper, we propose a novel inductive approach called MoRGH which first constructs a graph of similar movies by considering the information available in movies' plot synopsis and genres. Second, we construct a heterogeneous graph that includes two types of nodes: movies and users. This graph is built using the MovieLens dataset and the similarity graph generated in the first stage, where each edge between a user and a movie represents the user's rating for that movie, and each edge between two movies represents the similarity between them. Third, MoRGH mitigates the drawbacks of previous methods by employing a GNN and GAE-based model that combines collaborative and content-based approaches. This hybrid approach allows MoRGH to provide accurate and more personalized recommendations for each user, outperforming previous state-of-the-art models in terms of RMSE scores. The achieved improvement in RMSE scores demonstrates MoRGH's superior performance and its ability to deliver enhanced recommendations compared to existing models.

关键词： Movie recommender system graph neural network graph autoencoder graph inductive learning

来源：评论

学校读者我要写书评

暂无评论

Drug repositioning based on residual attention network and free multiscale adversarial training

引用

BMC BIOINFORMATICS 2024年第1期25卷 1页

作者： Li, Guanghui Li, Shuwen Liang, Cheng Xiao, Qiu Luo, Jiawei East China Jiaotong Univ Sch Informat Engn Nanchang Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China

BackgroundConducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently *** paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease *** comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor

关键词： graph attention network Residual network graph autoencoder Adversarial training Drug-disease association

来源：评论

学校读者我要写书评

暂无评论

Ensemble Clustering With Attentional Representation

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第2期36卷 581-593页

作者： Hao, Zhezheng Lu, Zhoumin Li, Guoxu Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence Sch Cybersecur Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Key Lab Intelligent Interact & Applicat Sch Artificial Intelligence Opt & Elect iOPENMini Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Key Lab Intelligent Interactionand Applicat Xian 710072 Shaanxi Peoples R China

Ensemble clustering has emerged as a powerful framework for analyzing heterogeneous and complex data. Despite the abundance of existing schemes, co-association matrix-based methods remain the mainstream approach. However, focusing solely on pairwise correlations falls short of fully capturing the intricate cluster relationships. Moreover, despite its potential, ensemble clustering has yet to effectively leverage the powerful representation capabilities of neural networks. To address these limitations, we propose a deep ensemble clustering method called Ensemble Clustering with Attentional Representation (ECAR). Our method considers the results of base partitions as groups with related information to explore higher-order fusion information. ECAR captures the importance of each sample's association with its related group by employing an attentional network, and encodes this information into a low-dimensional representation. The attentional network is trained by jointly optimizing the clustering loss from soft assignments learned from the embeddings and the reconstruction loss from the weighted graph generated from ensemble clustering. During training, the weights of base partitions are adaptively refined to promote diversity and consistency while reducing the impact of low-quality and redundant base partitions. Extensive experimental results on real-world datasets demonstrate the substantial improvement of our method over existing baseline ensemble clustering methods and deep clustering methods.

关键词： Deep clustering ensemble clustering graph autoencoder self-attentional unsupervised representation learning

来源：评论

学校读者我要写书评

暂无评论

Adversarial relationship graph learning soft sensor via negative information exclusion

引用

JOURNAL OF PROCESS CONTROL 2025年 145卷

作者： Jia, Mingwei Yang, Chao Pan, Zhouxin Liu, Qiang Liu, Yi Zhejiang Univ Technol Inst Proc Equipment & Control Engn Hangzhou 310023 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

The development of soft sensors in process industries necessitates learning the dynamic variable relationships caused by physicochemical reactions, whilst avoiding noise interference that degrades prediction performance and explainability. To address this, an adversarial relationship graph learning soft sensor is proposed, comprising both relationship learning and prediction modules. Irrelevant and false variable relationships caused by noise are treated as negative information, quantified through mutual information loss. They are captured by alternately adversarial training the self-attention network and graph autoencoder. By excluding negative information, a suitable variable relationship graph is constructed. The graph convolutional network then mines information from the data and relationships for accurate prediction. Two practical cases verify the model's physical consistency and demonstrate superior performance compared to several common models.

关键词： Soft sensor graph learning graph autoencoder graph convolutional network Self-attention mechanism Adversarial learning

来源：评论

学校读者我要写书评

暂无评论

Quantifying extreme failure scenarios in transportation systems with graph learning

引用

PATTERNS 2025年第4期6卷 101209页

作者： Guo, Mingxue Zhao, Tingting Gao, Jianxi Meng, Xin Gao, Ziyou Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China Rensselaer Polytech Inst Dept Comp Sci Troy NY 12180 USA

Statistical analysis of extreme events in complex engineering systems is essential for system design and reliability and resilience assessment. Due to the rarity of extreme events and the computational burden of system performance evaluation, estimating the probability of extreme failures is prohibitively expensive. Traditional methods, such as importance sampling, struggle with the high cost of deriving importance sampling densities for numerous components in large-scale systems. Here, we propose a graph learning approach, called importance sampling based on graph autoencoder (GAE-IS), to integrate a modified graph autoencoder model, termed a criticality assessor, with the cross-entropy-based importance sampling method. GAE-IS effectively decouples the criticality of components from their vulnerability to disastrous events in the workflow, demonstrating notable transferability and leading to significantly reduced computational costs of importance sampling in large-scale networks. The proposed methodology improves sampling efficiency by one to two orders of magnitude across several road networks and provides more accurate probability estimations.

关键词： extreme failure importance sampling graph autoencoder sampling efficiency road network

来源：评论

学校读者我要写书评

暂无评论

Drug repositioning framework using embedding drug-protein-disease similarities with graph convolution network and ensemble learning

引用

INTELLIGENT SYSTEMS WITH APPLICATIONS 2025年 25卷

作者： Torkey, Hanaa El-Behery, Heba Atti, Abdel-Fattah El-Fishawy, Nawal Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj Saudi Arabia Menoufia Univ Fac Elect Engn Comp Sci & Engn Dept Menoufia Egypt Kafrelsheikh Univ Fac Engn Dept Comp Sci & Engn Kafrelsheikh Egypt

The benefits of drug repositioning to the pharmaceutical industry have garnered significant attention in the field of drug development in recent years. Deep learning techniques have significantly improved drug repositioning by studying therapeutic drug profiles, diseases, and proteins. As the number of drugs increases, their targets and interactions generate imbalanced data, which may be undesirable as input to computational prediction model. The approach proposed in this paper uses a hierarchical network embedding technique and a graph autoencoder (GAE) scheme to solve this problem. The approach extracts embedding feature vectors of drugs and targets from a heterogeneous multi-source network to predict unknown drug-target interactions (DTIs). We employ a MetaPath instance that has extensive drug and target characteristic data. The effectiveness of utilizing Meta-Path instance, the number of attention heads, and graph Convolutional Network (GCN) and ensemble learning algorithm is analyzed on gold-standard datasets to evaluate the accuracy of the model and validity of the discovered DTI. The results achieved by our model using 10-fold cross-validation testing showed an improvement of 2.52 % in prediction accuracy, 4.2 % in recall, 3.94 % in AUC, and 3.6 % in F-score compared to state-of-theart methods.

关键词： Drug target Interactions graph autoencoder graph convolutional network Deep learning Drug-repositioning

来源：评论

学校读者我要写书评

暂无评论

Attributed graph Clustering Network with Adaptive Feature Fusion

引用

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2024年第10期E107A卷 1632-1636页

作者： Sun, Xuecheng Lu, Zheming Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou Peoples R China

To fully exploit the attribute information in graphs and dynamically fuse the features from different modalities, this letter proposes the Attributed graph Clustering Network with Adaptive Feature Fusion (AGC-AFF) for graph clustering, where an Attribute Reconstruction graph autoencoder (ARGAE) with masking operation learns to reconstruct the node attributes and adjacency matrix simultaneously, and an Adaptive Feature Fusion (AFF) mechanism dynamically fuses the features from different modules based on node attention. Extensive experiments on various benchmark datasets demonstrate the effectiveness of the proposed method.

关键词： graph clustering community detection graph Neural Network graph autoencoder

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：